Prospective evaluation of the IOTA logistic regression model LR2 for the diagnosis of ovarian cancer

Comparison of O-RADS with the ADNEX model and IOTA SR for risk stratification of adnexal lesions: a systematic review and meta-analysis

Purpose

This study aims to systematically compare the diagnostic performance of the Ovarian-Adnexal Reporting and Data System with the International Ovarian Tumor Analysis Simple Rules and the Assessment of Different NEoplasias in the adneXa model for risk stratification of ovarian cancer and adnexal masses.

Methods

A literature search of online databases for relevant studies up to July 2023 was conducted by two independent reviewers. The summary estimates were pooled with the hierarchical summary receiver-operating characteristic model. The quality of the included studies was assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 and the Quality Assessment of Diagnostic Accuracy Studies-Comparative Tool. Metaregression and subgroup analyses were performed to explore the impact of varying clinical settings.

Results

A total of 13 studies met the inclusion criteria. The pooled sensitivity and specificity for eight head-to-head studies between the Ovarian-Adnexal Reporting and Data System and the Assessment of Different NEoplasias in the adneXa model were 0.96 (95% CI 0.92-0.98) and 0.82 (95% CI 0.71-0.90) vs. 0.94 (95% CI 0.91-0.95) and 0.83 (95% CI 0.77-0.88), respectively, and for seven head-to-head studies between the Ovarian-Adnexal Reporting and Data System and the International Ovarian Tumor Analysis Simple Rules, the pooled sensitivity and specificity were 0.95 (95% CI 0.93-0.97) and 0.75 (95% CI 0.62-0.85) vs. 0.91 (95% CI 0.82-0.96) and 0.86 (95% CI 0.76-0.93), respectively. No significant differences were found between the Ovarian-Adnexal Reporting and Data System and the Assessment of Different NEoplasias in the adneXa model as well as the International Ovarian Tumor Analysis Simple Rules in terms of sensitivity (P = 0.57 and P = 0.21) and specificity (P = 0.87 and P = 0.12). Substantial heterogeneity was observed among the studies for all three guidelines.

Conclusion

All three guidelines demonstrated high diagnostic performance, and no significant differences in terms of sensitivity or specificity were observed between the three guidelines.

[Applicability of the Adnex score in predicting the malignancy of ovarian cysts]

OBJECTIVE

Ovarian cancer screening is a difficult problem due to the anatomy of the ovaries. Only histology allows a definite diagnosis. Our objective was to study the contribution of the Adnex score in the histological characterization of adnexal images for adequate management.

METHODS

It was a retrospective, mono-center, descriptive and analytical. Sixty-five patients were included, those operated for an ovarian cyst and meeting the Adnex criteria: clinical, ultrasound and laboratory.

RESULTS

The mean age of the patients was 38.6 years. They were nulliparous in 43 % of cases, and only four had a history of operation on ovarian cyst. Abdominal pelvic pain was the most frequent reason for consultation in 48 % of cases. An abdominopelvic mass was found on abdominal examination in 11 % of cases. Pelvic ultrasound made it possible to objectify the presence of an ovarian mass in all cases, with an average size of 79.66mm and a reassuring appearance in 66 % of cases. The calculation of the Adnex score was done in all patients preoperatively, for a 10 % cut-off, the model showed an 86 % chance of benignity for tumors proven to be histologically benign. The main route of entry was laparoscopy, in 61 % of cases. The treatment was in most cases conservative consisting essentially of a cystectomy.

CONCLUSION

The Adnex score discriminated well between benign and malignant masses, allowing for a better diagnosis preoperatively. It thus deserves its applicability in the clinical setting.

Comparative diagnostic accuracy of the IOTA SRR and LR2 scoring systems for discriminating between malignant and Benign Adnexal masses by junior physicians in Chinese patients: a retrospective observational study

BACKGROUND

The accuracy of ultrasound in distinguishing benign from malignant adnexal masses is highly correlated with the experience of ultrasound physicians. In China, most of ultrasound differentiation is done by junior physicians.

PURPOSE

To compare the diagnostic performance of the International Ovarian Tumour Analysis (IOTA) Simple Rules Risk (SRR) and IOTA Logistic Regression Model 2 (LR2) scoring systems in Chinese patients with adnexal masses.

METHODS

Retrospective analysis of ovarian cancer tumor patients who underwent surgery at a hospital in China from January 2016 to December 2021. Screening patients with at least one adnexal mass on inclusion and exclusion criteria. Two trained junior physicians evaluated each mass using the two scoring systems. A receiver operating characteristic curve was used to test the diagnostic performance of each system.

RESULTS

A total of 144 adnexal masses were retrospectively collected. Forty masses were histologically diagnosed as malignant. Compared with premenopausal women, postmenopausal women had a much higher rate of malignant masses. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of the SRR was 97.5% (95% CI: 86.8 -99.9%), 82.7% (95% CI: 74.0 -89.4%), 68.4% (95% CI: 58.7 -76.8%) and 98.9% (95% CI: 92.5 -99.8%). The sensitivity, specificity, PPV, NPV of the LR2 were 90.0% (95% CI: 76.5 -97.2%), 89.4% (95% CI: 81.9 -94.6%), 76.6% (95% CI: 65.0 -85.2%), and 95.9% (95% CI: 90.2 -98.3%). There was good agreement between two scoring systems, with 84.03% total agreement and a kappa value of 0.783 (95% CI: 0.70-0.864). The areas under the curve for predicting malignant tumours using SRR and LR2 were similar for all patients (P > 0.05 ).

CONCLUSION

The two scoring systems can effectively distinguish benign from malignant adnexal masses. Both scoring systems have high diagnostic efficacy, and diagnostic efficacy is stable, which can provide an important reference for clinical decision making.

Diagnostic Performances of Ultrasound-Based Models for Predicting Malignancy in Patients with Adnexal Masses

This study compared the diagnostic performance of different ultrasound-based models in discriminating between benign and malignant ovarian masses in a Filipino population. This was a prospective cohort study in women with findings of an ovarian mass on ultrasound. All included patients underwent a physical examination before level III specialist ultrasonographic and Doppler evaluation using the different International Ovarian Tumor Analysis (IOTA) Group’s risk models. Serum CA-125 and a second-generation multivariate index assay (MIA2G) were also determined for all patients. The ovarian imaging and biomarker results were correlated with the histological findings. A total of 260 patients with completed ultrasound, CA-125, MIA2G, and histopathologic results was included in the study. The presence of papillae with blood flow and irregular cyst walls during the ultrasound were significantly associated with a 20-fold (OR: 20.13, CI: 8.69−46.67, p < 0.01) and 10-fold (OR: 10.11, CI: 5.30−19.28, p < 0.01) increase in the likelihood of a malignant lesion, respectively. All individual sonologic procedures performed well in discerning malignant and benign ovarian lesions. IOTA-LR1 showed the highest accuracy (82.6%, 95% CI: 77.5−87%) for identifying ovarian cancer. IOTA-ADNEX showed the highest sensitivity (93.3%, 95% CI: 87.2−97.1%) while IOTA-LR2 exhibited the highest specificity (84.4%, 95% CI: 77.3−90%). Among the different serial test combinations, IOTA-LR1 with MIA2G and IOTA-LR2 with MIA2G showed the highest diagnostic accuracy (AUROC = 0.82). This study showed that all individual ultrasound-based models performed well in discerning malignant and benign ovarian lesions, with IOTA-LR1 exhibiting the highest accuracy.

Improving diagnostic strategies for ovarian cancer in Filipino women using ultrasound imaging and a multivariate index assay

OBJECTIVE

To evaluate the clinical performance and overall utility of imaging and biomarker assays in discriminating between benign and malignant ovarian masses in a Filipino population.

METHODS

This is a prospective cohort study among Filipino women undergoing assessment for an ovarian mass in a tertiary center. All included patients underwent a physical examination before level III specialist ultrasonographic and Doppler evaluation, multivariate index assay (MIA2G), and surgery for an adnexal mass. Ovarian tumors were classified as high-risk for malignancy based on the International Ovarian Tumour Analysis (IOTA) - Logistic Regression 2 (LR2) score. The ovarian imaging and biomarker results were correlated with the reference standard: histological findings.

RESULTS

Among the 379 women with adnexal masses enrolled in this study, 291 were evaluable with ultrasound imaging, biomarker assays, and histopathological results. The risk of malignancy was higher for women classified as high-risk based on IOTA-LR2 (≥10%). The sensitivity, specificity, and diagnostic accuracy for the prediction of malignancy were 81.2%, 81%, and 0.81 (95% CI: 0.77-0.86) for IOTA-LR2; 77.5%, 66.7%, and 0.72 (95% CI: 0.67-0.77) for CA-125; and 91.3%, 41.2%, and 0.66 (95% CI: 0.62-0.71) for MIA2G. A combination of IOTA-LR2 and MIA2G significantly influenced the diagnostic performance and the result. When MIA2G was combined with IOTA-LR2 in parallel, the sensitivity (94.2%) and NPV (87.7%) increased, but the specificity (37.3%) decreased. When combined with IOTA-LR2 in series, there were fewer false positives, which resulted in improved specificity (85%).

CONCLUSION

This study determined the utility of ovarian imaging and a second-generation multivariate index assay in predicting the risk of ovarian malignancy. IOTA-LR2 and MIA2G were useful in classifying patients with a high risk for ovarian malignancy.

A comparison of the diagnostic performance of the O-RADS, RMI4, IOTA LR2, and IOTA SR systems by senior and junior doctors

Purpose

This study compared the diagnostic performance of the Ovarian-Adnexal Reporting and Data System (O-RADS), the Risk of Malignancy Index 4 (RMI4), the International Ovarian of Tumor Analysis Logistic Regression Model 2 (IOTA LR2), and the IOTA Simple Rules (IOTA SR) in predicting the malignancy of adnexal masses (AMs).

Methods

This retrospective study included 575 women with AMs between 2017 and 2020. All clinical messages, ultrasound images, and pathological findings were collected. Two senior doctors (group I) and two junior doctors (group II) used the four systems to classify AMs. The postoperative pathological diagnosis was used as the gold standard to evaluate the diagnostic efficiency. A receiver operating characteristic curve was used to test the diagnostic performance. The interrater agreement between the two groups was tested using kappa values.

Results

Of all 592 AMs, 447 (75.5%) were benign, 123 (20.8%) were malignant, and 22 (3.7%) were borderline. The intergroup consistency test yielded kappa values of 0.71, 0.92, 0.68, and 0.77 for the O-RADS, RMI4, IOTA LR2, and IOTA SR, respectively. To predict malignant lesions, the areas under the curve of the O-RADS, RMI4, IOTA LR2, and IOTA SR systems were 0.90, 0.89, 0.90, and 0.86 for group I and 0.89, 0.87, 0.88, and 0.84 for group II, respectively. The O-RADS had the highest sensitivity (91.0% in group I and 84.8% in group II).

Conclusion

The four diagnostic systems could compensate for junior doctors’ inexperience in predicting malignant adnexal lesions. The O-RADS performed best and showed the highest sensitivity.

Machine Learning Analysis of Immune Cells for Diagnosis and Prognosis of Cutaneous Melanoma

Tumor infiltration, known to associate with various cancer initiations and progressions, is a promising therapeutic target for aggressive cutaneous melanoma. Then, the relative infiltration of 24 kinds of immune cells in melanoma was assessed by a single sample gene set enrichment analysis (ssGSEA) program from a public database. The multiple machine learning algorithms were applied to evaluate the efficiency of immune cells in diagnosing and predicting the prognosis of melanoma. In comparison with the expression of immune cell in tumor and normal control, we built the immune diagnostic models in training dataset, which can accurately classify melanoma patients from normal (LR AUC = 0.965, RF AUC = 0.99, SVM AUC = 0.963, LASSO AUC = 0.964, and NNET AUC = 0.989). These diagnostic models were also validated in three outside datasets and suggested over 90% AUC to distinguish melanomas from normal patients. Moreover, we also developed a robust immune cell biomarker that could estimate the prognosis of melanoma. This biomarker was also further validated in internal and external datasets. Following that, we created a nomogram with a composition of risk score and clinical parameters, which had high accuracies in predicting survival over three and five years. The nomogram's decision curve revealed a bigger net benefit than the tumor stage. Furthermore, a risk score system was used to categorize melanoma patients into high- and low-risk subgroups. The high-risk group has a significantly lower life expectancy than the low-risk subgroup. Finally, we observed that complement, epithelial-mesenchymal transition, and inflammatory response were significantly activated in the high-risk group. Therefore, the findings provide new insights for understanding the tumor infiltration relevant to clinical applications as a diagnostic or prognostic biomarker for melanoma.

O-RADS for Ultrasound: A User's Guide, From the AJR Special Series on Radiology Reporting and Data Systems

The Ovarian-Adnexal Reporting and Data System (O-RADS) is a lexicon and risk stratification tool designed for the accurate characterization of adnexal lesions and is essential for optimal patient management. O-RADS is a recent addition to the American College of Radiology (ACR) reporting and data systems and consists of ultrasound (US) and MRI arms. Since most ovarian or adnexal lesions are first detected with US, O-RADS US is considered the primary assessment tool. Application of O-RADS US is recommended whenever a nonphysiologic lesion is encountered. Lesion characterization may be streamlined by use of an algorithmic approach focused on relevant features and an abbreviated version of the lexicon. Resources to expedite O-RADS US categorization and determination of a management recommendation include easy online access to the ACR color-coded risk stratification scorecards and an O-RADS US calculator that is available as a smartphone app. Reporting should be concise and include relevant features for risk stratification that adhere to lexicon terminology. Technical considerations include optimization of gray-scale and color Doppler technique and performance of problem-solving maneuvers to help avoid common pitfalls. This review provides a user-friendly summary of O-RADS US with practical tips for everyday clinical use.

Diagnostic accuracy of transvaginal ultrasound examination for assigning a specific diagnosis to adnexal masses: A meta-analysis

Transvaginal ultrasound (TVUS) is a standard imaging modality for differentiating patients with benign or malignant suspected adnexal mass. To date, numerous studies have assessed the diagnostic accuracy of TVUS in various settings but with variable results. Therefore, the purpose of the present study was to perform a meta-analysis to evaluate the diagnostic accuracy of TVUS for the differentiation of adnexal masses. An electronic search in the Medline, Scopus, Cochrane and Embase databases from inception till November 2019 was carried out. Meta-analysis was performed to obtain pooled sensitivity and specificity of TVUS to distinguish malignant from benign adnexal masses. The quality assessment of diagnostic accuracy studies-2 tool was used to assess the quality of trials. A total of 41 studies with 18,391 patients were included. The pooled sensitivity and specificity of TVUS was 92% (95% CI: 90-94%) and 89% (95% CI: 85-92%), respectively. The area under the receiver operating characteristic curve was 0.96 (95% CI: 0.84-1.00). There was considerable heterogeneity with a statistically significant chi-square test (P<0.001) and I² of 99%. Meta-regression results indicated that index test standards, patient selection bias and study design were potential sources of heterogeneity (P<0.05). The funnel plot was symmetrical and low publication bias was confirmed by an insignificant Deek's test (P=0.90). The present systematic review and meta-analysis indicated that TVUS is useful in differentiating between benign and malignant tumours among patients with suspected adnexal mass with high sensitivity and specificity.

Scoring systems for the evaluation of adnexal masses nature: current knowledge and clinical applications

Adnexal masses are a common finding in women, with 20% of them developing at least one pelvic mass during their lifetime. There are more than 30 different subtypes of adnexal tumours, with multiple different subcategories, and the correct characterisation of the pelvic masses is of paramount importance to guide the correct management. On that basis, different algorithms and scoring systems have been developed to guide the clinical assessment. The first scoring system implemented into the clinical practice was the Risk of Malignancy Index, which combines ultrasound evaluation, menopausal status, and serum CA-125 levels. Today, current guidelines regarding female patients with adnexal masses include the application of International Ovarian Tumours Analysis simple rules, logistic regression model 1 (LR1) and LR2, OVERA, cancer ovarii non-invasive assessment of treating strategy, and assessment of Different Neoplasias in the adnexa. In this scenario, the choice of the scoring system for the discrimination between benign and malignant ovarian tumours can be complex when approaching patients with adnexal masses. This review aims to summarise the available evidence regarding the different scoring systems to provide a complete overview of the topic.

Development and Validation of a Predictive Score for Preoperative Diagnosis of Early Stage Epithelial Ovarian Cancer

Objective: To develop and validate a simplified multi-parameter risk-based scoring system for preoperative diagnosis of early stage epithelial ovarian cancer. Methods: All women presented with adnexal mass and were scheduled for operation at Phrapokklao hospital during September 2013 – December 2017 were included and categorized according to their histopathologic reports into early stage ovarian cancer groups and benign ovarian tumor groups. Multivariable logistic regression was used to explore for potential predictors. The selected logistic coefficients were transformed into risk-based scoring system. Internal validation was done with bootstrapping procedure. Results: A total of 270 participants were included in analysis and predictive model development, 54 in early stage ovarian cancer group and 216 in benign ovarian tumor group. Menopausal status, two abnormal ultrasound findings (presence of solid component or ascites), tumor size and serum CA-125 level were used for derivation of the scoring system. The score-based model showed area under ROC of 0.88 (95%CI 0.82-0.93). The developed scoring system ranged from 0 to 51 was classified into 3 subcategories for clinical practicability. The positive predictive values for the presence of early stage ovarian cancer were 2.07 (95%CI 0.43-6.05) for low risk patient, 29.13(95%CI 19.65-41.58) for moderate risk patient, and 95.45(95%CI 77.16-99.88) for high risk patient. Conclusion: This simplified risk-based scoring system for preoperative diagnosis of early stage ovarian cancer could aid general physicians or general gynecologists in evaluation of patients presenting with ovarian tumors and help gynecologic oncologists in management planning and prioritization of patients for operation.

[Diagnostic value of imaging (ultrasonography, doppler, CT, MR, PET-CT) for the diagnosis of a suspicious ovarian mass and staging of ovarian, tubal or primary peritoneal cancer: Article drafted from the French Guidelines in oncology entitled "Initial management of patients with epithelial ovarian cancer" developed by FRANCOGYN, CNGOF, SFOG, GINECO-ARCAGY under the aegis of CNGOF and endorsed by INCa]

Transvaginal ultrasound is the first-line examination allowing characterizing 80 to 90% of adnexal masses (LP1). If performed by an expert, a subjective analysis is optimal. If performed by a non-expert, combining the use of Simple Rules with subjective analysis can achieve the diagnostic performance of an expert (LP1). Whichever the chosen model (subjective analysis by an expert or combination of the Simple Rules with a subjective analysis by a non-expert), a second-line examination will have to be proposed in the complex or indeterminate cases (about 20% of the masses) (grade A). The best-performing second-line test for characterization is pelvic MRI (LP1). If read by an expert, a pathological hypothesis can or should be suggested (grade D). In case of non-expert reading, the use of the ADNEXMR score allows a reliable assessment of the positive predictive value of malignancy to guide the patient towards the best management (gradeC). For preoperative assessment and evaluation of resectability of ovarian, fallopian tube or primary peritoneal cancer, it is recommended to perform a chest abdomen and pelvis CT with contrast agent injection (LP2, grade B). In the event of a contraindication to the injection of iodinated contrast agent (severe renal insufficiency, GFR <30mL/min), an abdomen and pelvis MRI completed with a non-injected chest CT may be proposed (LP3, grade C). By analogy, the same examinations are recommended to evaluate the disease after neo-adjuvant chemotherapy (LP3, Recommendation grade C). Further studies will be required to determine whether PET-CT provides better lymph node assessment before retroperitoneal and pelvic lymphadenectomy. PET-CT may be used to eliminate lymph node involvement in the absence of suspicious lymph nodes on morphological examination (LP3, grade C). The report should specify the localizations leading to a risk of incomplete cytoreductive surgery and lesions outside the field explored during surgery.

Prospective evaluation of IOTA logistic regression models LR1 and LR2 in comparison with subjective pattern recognition for diagnosis of ovarian cancer in an outpatient setting

OBJECTIVE

To determine whether International Ovarian Tumor Analysis (IOTA) logistic regression models LR1 and LR2 developed for the preoperative diagnosis of ovarian cancer could also be used to differentiate between benign and malignant adnexal tumors in the population of women attending gynecology outpatient clinics.

METHODS

This was a single-center prospective observational study of consecutive women attending our gynecological diagnostic outpatient unit, recruited between May 2009 and January 2012. All the women were first examined by a Level-II ultrasound operator. In those diagnosed with adnexal tumors, the IOTA-LR1/2 protocol was used to evaluate the masses. The LR1 and LR2 models were then used to assess the risk of malignancy. Subsequently, the women were also examined by a Level-III examiner, who used pattern recognition to differentiate between benign and malignant tumors. Women with an ultrasound diagnosis of malignancy were offered surgery, while asymptomatic women with presumed benign lesions were offered conservative management with a minimum follow-up of 12 months. The initial diagnosis was compared with two reference standards: histological findings and/or a comparative assessment of tumor morphology on follow-up ultrasound scans. All women for whom the tumor classification on follow-up changed from benign to malignant were offered surgery.

RESULTS

In the final analysis, 489 women who had either or both of the reference standards were included. Their mean age was 50 years (range, 16-91 years) and 45% were postmenopausal. Of the included women, 342/489 (69.9%) had surgery and 147/489 (30.1%) were managed conservatively. The malignancy rate was 137/489 (28.0%). Overall, sensitivities of LR1 and LR2 for the diagnosis of malignancy were 97.1% (95% CI, 92.7-99.2%) and 94.9% (95% CI, 89.8-97.9%) and specificities were 77.3% (95% CI, 72.5-81.5%) and 76.7% (95% CI, 71.9-81.0%), respectively (P > 0.05). In comparison with pattern recognition (sensitivity 94.2% (95% CI, 88.8-97.4%), specificity 96.3% (95% CI, 93.8-98.0%)), the specificities of the IOTA models were significantly lower (P < 0.0001). A significantly higher number of women would have been offered surgery for suspected cancer if the women had been assessed using the IOTA models instead of pattern recognition (213/489 (43.6%) vs 142/489 (29.0%); P < 0.001).

CONCLUSIONS

The IOTA models maintained their high sensitivity when used in an outpatient setting. Specificity was relatively low, which indicates that a significant proportion of the women would have been offered unnecessary surgery for suspected ovarian cancer. These findings show that the IOTA models could be used as a first-stage test to diagnose ovarian cancer in an outpatient setting, but a different second-stage test is required to minimize the number of false-positive findings. Copyright © 2017 ISUOG. Published by John Wiley & Sons Ltd.

Sonographic Diagnosis of Tubal Cancer with IOTA Simple Rules Plus Pattern Recognition

Objective:

To evaluate diagnostic performance of IOTA simple rules plus pattern recognition in predicting tubal cancer.

Methods:

Secondary analysis was performed on prospective database of our IOTA project. The patients recruited in the project were those who were scheduled for pelvic surgery due to adnexal masses. The patients underwent ultrasound examinations within 24 hours before surgery. On ultrasound examination, the masses were evaluated using the well-established IOTA simple rules plus pattern recognition (sausage-shaped appearance, incomplete septum, visible ipsilateral ovaries) to predict tubal cancer. The gold standard diagnosis was based on histological findings or operative findings.

Results:

A total of 482 patients, including 15 cases of tubal cancer, were evaluated by ultrasound preoperatively. The IOTA simple rules plus pattern recognition gave a sensitivity of 86.7% (13 in 15) and specificity of 97.4%. Sausage-shaped appearance was identified in nearly all cases (14 in 15). Incomplete septa and normal ovaries could be identified in 33.3% and 40%, respectively.

Conclusion:

IOTA simple rules plus pattern recognition is relatively effective in predicting tubal cancer. Thus, we propose the simple scheme in diagnosis of tubal cancer as follows. First of all, the adnexal masses are evaluated with IOTA simple rules. If the B-rules could be applied, tubal cancer is reliably excluded. If the M-rules could be applied or the result is inconclusive, careful delineation of the mass with pattern recognition should be performed.

Evaluation of IOTA Simple Ultrasound Rules to Distinguish Benign and Malignant Ovarian Tumours

INTRODUCTION

IOTA stands for International Ovarian Tumour Analysis group. Ovarian cancer is one of the common cancers in women and is diagnosed at later stage in majority. The limiting factor for early diagnosis is lack of standardized terms and procedures in gynaecological sonography. Introduction of IOTA rules has provided some consistency in defining morphological features of ovarian masses through a standardized examination technique.

AIM

To evaluate the efficacy of IOTA simple ultrasound rules in distinguishing benign and malignant ovarian tumours and establishing their use as a tool in early diagnosis of ovarian malignancy.

MATERIALS AND METHODS

A hospital based case control prospective study was conducted. Patients with suspected ovarian pathology were evaluated using IOTA ultrasound rules and designated as benign or malignant. Findings were correlated with histopathological findings. Collected data was statistically analysed using chi-square test and kappa statistical method.

RESULTS

Out of initial 55 patients, 50 patients were included in the final analysis who underwent surgery. IOTA simple rules were applicable in 45 out of these 50 patients (90%). The sensitivity for the detection of malignancy in cases where IOTA simple rules were applicable was 91.66% and the specificity was 84.84%. Accuracy was 86.66%. Classifying inconclusive cases as malignant, the sensitivity and specificity was 93% and 80% respectively. High level of agreement was found between USG and histopathological diagnosis with Kappa value as 0.323.

CONCLUSION

IOTA simple ultrasound rules were highly sensitive and specific in predicting ovarian malignancy preoperatively yet being reproducible, easy to train and use.

[Before surgery predictability of malignant ovarian tumors based on ADNEX model and its use in clinical practice]

OBJECTIVE

The principal aim of this study was the predictability of malignant ovarian tumors and to determine a cut-off value for this score to indicate the risk of malignancy that would be easy to use in clinical practice.

METHODS

We retrospectively calculated the ADNEX score for all patients who underwent surgery for ovarian tumours in two Burgundy hospitals (Dijon University Hospital and Chalon-sur-Saône Hospital). We used the nine criteria of the ADNEX model. The inclusion criteria were the presence of all of the ADNEX criteria and a histology result. We analysed the sensitivity, specificity, PPV and PNV of four cut-offs (3%, 5%, 10% and 15%) for the entire pool then by age groups; from 14 to 42 (group 1) and 43 and more (group 2) RESULTS: Two hundred and eighty-four patients managed for an ovarian tumour were included between the 1st January 2013 and the 31st December 2015. Our AUC was of 0.94 (95% CI [0.903-0.977]) for discrimination between benign and malignant ovarian tumors. For a cut-off of 10%, sensitivity was 90%, specificity was 81.1%, PPV was 34.6% and PNV 98.5%. Results were lower for young women than for the second group. For a cut-off of 10%, group 1 had a sensitivity of 77.7% and specificity of 89.6%, PPV of 46.6% and PNV 97.5%. For the group 2, sensitivity was 95.2%, specificity was 76.6%, PPV was 33.8% and PNV was 99.2%. The most reasonable cut-off for the whole pool was 10%. For group 1 a cut-off of 5% was retained due to the less satisfying detection of "borderline" tumours more frequent in younger patients. For group 2 the cut-off of 10% gave the best results.

CONCLUSION

In our study, a lower cut-off for younger women seemed better suited to discriminate borderline tumours. In practice, the ADNEX score associated with the peroperative laparoscopic examination seems to be the best way to use the ADNEX model. Our study showed that the ADNEX model allows a good predictability of malignant ovarian tumours. The predictability becomes less satisfying for the youngest patients. A cut-off malignity value allowing surgical treatment of patients in a specialised facility was reached for two age groups: a cut-off of 5% for women under 42 years old and a cut-off of 10% for women over 43 years old.

IOTA Simple Rules in Differentiating between Benign and Malignant Adnexal Masses by Non-expert Examiners

OBJECTIVE

To evaluate the diagnostic performance of IOTA simple rules in predicting malignant adnexal tumors by non-expert examiners.

MATERIALS AND METHODS

Five obstetric/gynecologic residents, who had never performed gynecologic ultrasound examination by themselves before, were trained for IOTA simple rules by an experienced examiner. One trained resident performed ultrasound examinations including IOTA simple rules on 100 women, who were scheduled for surgery due to ovarian masses, within 24 hours of surgery. The gold standard diagnosis was based on pathological or operative findings. The five-trained residents performed IOTA simple rules on 30 patients for evaluation of inter-observer variability.

RESULTS

A total of 100 patients underwent ultrasound examination for the IOTA simple rules. Of them, IOTA simple rules could be applied in 94 (94%) masses including 71 (71.0%) benign masses and 29 (29.0%) malignant masses. The diagnostic performance of IOTA simple rules showed sensitivity of 89.3% (95%CI, 77.8%; 100.7%), specificity 83.3% (95%CI, 74.3%; 92.3%). Inter-observer variability was analyzed using Cohen's kappa coefficient. Kappa indices of the four pairs of raters are 0.713-0.884 (0.722, 0.827, 0.713, and 0.884).

CONCLUSIONS

IOTA simple rules have high diagnostic performance in discriminating adnexal masses even when are applied by non-expert sonographers, though a training course may be required. Nevertheless, they should be further tested by a greater number of general practitioners before widely use.

Usefulness of diagnostic indices comprising clinical, sonographic, and biomarker data for discriminating benign from malignant ovarian masses

The objective of this study was to review the accuracy of indices combining several diagnostic variables, in comparison to other models, sonography alone, and biomarker assays, for predicting benign or malignant ovarian lesions. Different single modalities were reviewed. The most useful complex models were International Ovarian Tumor Analysis (IOTA) sonographic logistic regression model 2 (area under the curve, 0.949), risk of malignancy index-cancer antigen 125-human epididymis protein 4 (0.950), risk of malignancy algorithm (0.953), pelvic mass score (0.960), non-IOTA logistic regression model (0.970), and histoscanning score logistic regression model (0.970). None of the indices was superior to an expert subjective sonographic assessment (0.968). For women with adnexal tumors, indices with high accuracy are available that are applicable in clinical practice and comparable to an expert subjective sonographic assessment for discriminating benign from malignant masses.

Interobserver agreement in describing the ultrasound appearance of adnexal masses and in calculating the risk of malignancy using logistic regression models

PURPOSE

To estimate interobserver agreement with regard to describing adnexal masses using the International Ovarian Tumor Analysis (IOTA) terminology and the risk of malignancy calculated using IOTA logistic regression models LR1 and LR2, and to elucidate what explained the largest interobserver differences in calculated risk of malignancy.

EXPERIMENTAL DESIGN

One hundred and seventeen women with adnexal masses were examined with transvaginal gray scale and power Doppler ultrasound by two independent experienced sonologists who described the masses using IOTA terminology. The risk of malignancy was calculated using LR1 and LR2. A predetermined risk of malignancy cutoff of 10% indicated malignancy.

RESULTS

There were 94 benign, four borderline, and 19 invasively malignant tumors. There was substantial variability between the two sonologists in measurement results and some variability in assessment of categorical variables (agreement 40%-98%, Kappa 0.30-0.91). Interobserver agreement when classifying tumors as benign or malignant was 84% (98/117), Kappa 0.68 for LR1, and for LR2 85% (99/117), Kappa 0.68. When using LR1 and LR2, the interobserver difference in calculated risk was ≥ 25 percentage units in 9% (11/117) and 12% (14/117) of tumors, respectively. Differences in assessment of wall irregularity, acoustic shadowing, color score, and color flow in papillary projections explained most of these largest differences.

CONCLUSIONS

Interobserver agreement in classifying tumors as benign or malignant using the risk of malignancy cutoff of 10% for LR1 and LR2 was good. However, because risk estimates may differ substantially between sonologists, one should be cautious with using the risk value for counseling patients about their individual risk.

Distinguishing benign from malignant pelvic mass utilizing an algorithm with HE4, menopausal status, and ultrasound findings

Objective

The purpose of this study was to develop a risk prediction score for distinguishing benign ovarian mass from malignant tumors using CA-125, human epididymis protein 4 (HE4), ultrasound findings, and menopausal status. The risk prediction score was compared to the risk of malignancy index and risk of ovarian malignancy algorithm (ROMA).

Methods

This was a prospective, multicenter (n=6) study with patients from six Asian countries. Patients had a pelvic mass upon imaging and were scheduled to undergo surgery. Serum CA-125 and HE4 were measured on preoperative samples, and ultrasound findings were recorded. Regression analysis was performed and a risk prediction model was developed based on the significant factors. A bootstrap technique was applied to assess the validity of the HE4 model.

Results

A total of 414 women with a pelvic mass were enrolled in the study, of which 328 had documented ultrasound findings. The risk prediction model that contained HE4, menopausal status, and ultrasound findings exhibited the best performance compared to models with CA-125 alone, or a combination of CA-125 and HE4. This model classified 77.2% of women with ovarian cancer as medium or high risk, and 86% of women with benign disease as very-low, low, or medium-low risk. This model exhibited better sensitivity than ROMA, but ROMA exhibited better specificity. Both models performed better than CA-125 alone.

Conclusion

Combining ultrasound with HE4 can improve the sensitivity for detecting ovarian cancer compared to other algorithms.

In 2014, can we do better than CA125 in the early detection of ovarian cancer?

Ovarian cancer is a lethal gynecologic malignancy with greater than 70% of women presenting with advanced stage disease. Despite new treatments, long term outcomes have not significantly changed in the past 30 years with the five-year overall survival remaining between 20% and 40% for stage III and IV disease. In contrast patients with stage I disease have a greater than 90% five-year overall survival. Detection of ovarian cancer at an early stage would likely have significant impact on mortality rate. Screening biomarkers discovered at the bench have not translated to success in clinical trials. Existing screening modalities have not demonstrated survival benefit in completed prospective trials. Advances in high throughput screening are making it possible to evaluate the development of ovarian cancer in ways never before imagined. Data in the form of human “-omes” including the proteome, genome, metabolome, and transcriptome are now available in various packaged forms. With the correct pooling of resources including prospective collection of patient specimens, integration of high throughput screening, and use of molecular heterogeneity in biomarker discovery, we are poised to make progress in ovarian cancer screening. This review will summarize current biomarkers, imaging, and multimodality screening strategies in the context of emerging technologies.

A prospective validation of the IOTA logistic regression models (LR1 and LR2) in comparison to subjective pattern recognition for the diagnosis of ovarian cancer

OBJECTIVES

This study aimed to assess the accuracy of the International Ovarian Tumour Analysis (IOTA) logistic regression models (LR1 and LR2) and that of subjective pattern recognition (PR) for the diagnosis of ovarian cancer.

METHODS AND MATERIALS

This was a prospective single-center study in a general gynecology unit of a tertiary hospital during 33 months. There were 292 consecutive women who underwent surgery after an ultrasound diagnosis of an adnexal tumor. All examinations were by a single level 2 ultrasound operator, according to the IOTA guidelines. The malignancy likelihood was calculated using the IOTA LR1 and LR2. The women were then examined separately by an expert operator using subjective PR. These were compared to operative findings and histology. The sensitivity, specificity, area under the curve (AUC), and accuracy of the 3 methods were calculated and compared.

RESULTS

The AUCs for LR1 and LR2 were 0.94 [95% confidence interval (CI), 0.92-0.97] and 0.93 (95% CI, 0.90-0.96), respectively. Subjective PR gave a positive likelihood ratio (LR+ve) of 13.9 (95% CI, 7.84-24.6) and a LR-ve of 0.049 (95% CI, 0.022-0.107). The corresponding LR+ve and LR-ve for LR1 were 3.33 (95% CI, 2.85-3.55) and 0.03 (95% CI, 0.01-0.10), and for LR2 were 3.58 (95% CI, 2.77-4.63) and 0.052 (95% CI, 0.022-0.123). The accuracy of PR was 0.942 (95% CI, 0.908-0.966), which was significantly higher when compared with 0.829 (95% CI, 0.781-0.870) for LR1 and 0.836 (95% CI, 0.788-0.872) for LR2 (P < 0.001).

CONCLUSIONS

The AUC of the IOTA LR1 and LR2 were similar in nonexpert's hands when compared to the original and validation IOTA studies. The PR method was the more accurate test to diagnose ovarian cancer than either of the IOTA models.

Strategies to diagnose ovarian cancer: new evidence from phase 3 of the multicentre international IOTA study

Background:

To compare different ultrasound-based international ovarian tumour analysis (IOTA) strategies and risk of malignancy index (RMI) for ovarian cancer diagnosis using a meta-analysis approach of centre-specific data from IOTA3.

Methods:

This prospective multicentre diagnostic accuracy study included 2403 patients with 1423 benign and 980 malignant adnexal masses from 2009 until 2012. All patients underwent standardised transvaginal ultrasonography. Test performance of RMI, subjective assessment (SA) of ultrasound findings, two IOTA risk models (LR1 and LR2), and strategies involving combinations of IOTA simple rules (SRs), simple descriptors (SDs) and LR2 with and without SA was estimated using a meta-analysis approach. Reference standard was histology after surgery.

Results:

The areas under the receiver operator characteristic curves of LR1, LR2, SA and RMI were 0.930 (0.917–0.942), 0.918 (0.905–0.930), 0.914 (0.886–0.936) and 0.875 (0.853–0.894). Diagnostic one-step and two-step strategies using LR1, LR2, SR and SD achieved summary estimates for sensitivity 90–96%, specificity 74–79% and diagnostic odds ratio (DOR) 32.8–50.5. Adding SA when IOTA methods yielded equivocal results improved performance (DOR 57.6–75.7). Risk of Malignancy Index had sensitivity 67%, specificity 91% and DOR 17.5.

Conclusions:

This study shows all IOTA strategies had excellent diagnostic performance in comparison with RMI. The IOTA strategy chosen may be determined by clinical preference.

Presurgical diagnosis of adnexal tumours using mathematical models and scoring systems: a systematic review and meta-analysis

BACKGROUND Characterizing ovarian pathology is fundamental to optimizing management in both pre- and post-menopausal women. Inappropriate referral to oncology services can lead to unnecessary surgery or overly radical interventions compromising fertility in young women, whilst the consequences of failing to recognize cancer significantly impact on prognosis. By reflecting on recent developments of new diagnostic tests for preoperative identification of malignant disease in women with adnexal masses, we aimed to update a previous systematic review and meta-analysis. METHODS An extended search was performed in MEDLINE (PubMed) and EMBASE (OvidSp) from March 2008 to October 2013. Eligible studies provided information on diagnostic test performance of models, designed to predict ovarian cancer in a preoperative setting, that contained at least two variables. Study selection and extraction of study characteristics, types of bias, and test performance was performed independently by two reviewers. Quality was assessed using a modified version of the QUADAS assessment tool. A bivariate hierarchical random effects model was used to produce summary estimates of sensitivity and specificity with 95% confidence intervals or plot summary ROC curves for all models considered. RESULTS Our extended search identified a total of 1542 new primary articles. In total, 195 studies were eligible for qualitative data synthesis, and 96 validation studies reporting on 19 different prediction models met the predefined criteria for quantitative data synthesis. These models were tested on 26 438 adnexal masses, including 7199 (27%) malignant and 19 239 (73%) benign masses. The Risk of Malignancy Index (RMI) was the most frequently validated model. The logistic regression model LR2 with a risk cut-off of 10% and Simple Rules (SR), both developed by the International Ovarian Tumor Analysis (IOTA) study, performed better than all other included models with a pooled sensitivity and specificity, respectively, of 0.92 [95% CI 0.88-0.95] and 0.83 [95% CI 0.77-0.88] for LR2 and 0.93 [95% CI 0.89-0.95] and 0.81 [95% CI 0.76-0.85] for SR. A meta-analysis of centre-specific results stratified for menopausal status of two multicentre cohorts comparing LR2, SR and RMI-1 (using a cut-off of 200) showed a pooled sensitivity and specificity in premenopausal women for LR2 of 0.85 [95% CI 0.75-0.91] and 0.91 [95% CI 0.83-0.96] compared with 0.93 [95% CI 0.84-0.97] and 0.83 [95% CI 0.73-0.90] for SR and 0.44 [95% CI 0.28-0.62] and 0.95 [95% CI 0.90-0.97] for RMI-1. In post-menopausal women, sensitivity and specificity of LR2, SR and RMI-1 were 0.94 [95% CI 0.89-0.97] and 0.70 [95% CI 0.62-0.77], 0.93 [95% CI 0.88-0.96] and 0.76 [95% CI 0.69-0.82], and 0.79 [95% CI 0.72-0.85] and 0.90 [95% CI 0.84-0.94], respectively. CONCLUSIONS An evidence-based approach to the preoperative characterization of any adnexal mass should incorporate the use of IOTA Simple Rules or the LR2 model, particularly for women of reproductive age.

Multicentre external validation of IOTA prediction models and RMI by operators with varied training

Background:

Correct characterisation of ovarian tumours is critical to optimise patient care. The purpose of this study is to evaluate the diagnostic performance of the International Ovarian Tumour Analysis (IOTA) logistic regression model (LR2), ultrasound Simple Rules (SR), the Risk of Malignancy Index (RMI) and subjective assessment (SA) for preoperative characterisation of adnexal masses, when ultrasonography is performed by examiners with different background training and experience.

Methods:

A 2-year prospective multicentre cross-sectional study. Thirty-five level II ultrasound examiners contributed in three UK hospitals. Transvaginal ultrasonography was performed using a standardised approach. The final outcome was the surgical findings and histological diagnosis. To characterise the adnexal masses, the six-variable prediction model (LR2) with a cutoff of 0.1, the RMI with cutoff of 200, ten SR (five rules for malignancy and five rules for benignity) and SA were applied. The area under the curves (AUCs) for performance of LR2 and RMI were calculated. Diagnostic performance measures for all models assessed were sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR−), and the diagnostic odds ratio (DOR).

Results:

Nine-hundred and sixty-two women with adnexal masses underwent transvaginal ultrasonography, whereas 255 had surgery. Prevalence of malignancy was 29% (49 primary invasive epithelial ovarian cancers, 18 borderline ovarian tumours, and 7 metastatic tumours). The AUCs for LR2 and RMI for all masses were 0.94 (95% confidence interval (CI): 0.89–0.97) and 0.90 (95% CI: 0.83–0.94), respectively. In premenopausal women, LR2−RMI difference was 0.09 (95% CI: 0.03–0.15) compared with −0.02 (95% CI: −0.08 to 0.04) in postmenopausal women. For all masses, the DORs for LR2, RMI, SR+SA (using SA when SR inapplicable), SR+MA (assuming malignancy when SR inapplicable), and SA were 62 (95% CI: 27–142), 43 (95% CI: 19–97), 109 (95% CI: 44–274), 66 (95% CI: 27–158), and 70 (95% CI: 30–163), respectively.

Conclusion:

Overall, the test performance of IOTA prediction models and rules as well as the RMI was maintained in examiners with varying levels of training and experience.

A comparison between an ultrasound based prediction model (LR2) and the risk of ovarian malignancy algorithm (ROMA) to assess the risk of malignancy in women with an adnexal mass

OBJECTIVE

The identification of novel biomarkers led to the development of the ROMA algorithm incorporating both HE4 and CA125 to predict malignancy in women with a pelvic mass. An ultrasound based prediction model (LR2) developed by the International Ovarian Tumor Analysis (IOTA) study offers better diagnostic performance than CA125 alone. In this study we compared the diagnostic accuracy between LR2 and ROMA.

METHODS

This study included women with a pelvic mass scheduled for surgery and enrolled in a previous prospective diagnostic accuracy study. Experienced ultrasound examiners, general gynecologists and trainees supervised by one of the experts performed the preoperative transvaginal ultrasound examinations. Serum biomarkers were taken prior to surgery. Accuracy of LR2 and ROMA was estimated at completion of this study and did not form part of the decision making process. Final outcome was histology of removed tissues and surgical stage if relevant.

RESULTS

In total 360 women were evaluated. 216 women had benign disease and 144 a malignancy. Overall test performance of LR2 (AUC 0.952) with 94% sensitivity and 82% specificity was significantly better than ROMA (AUC 0.893) with 84% sensitivity and 80% specificity. Difference in AUC was 0.059 (95% CI: 0.026-0.091; P-value 0.0004). Similar results were obtained when stratified for menopausal status.

CONCLUSION

LR2 shows a better diagnostic performance than ROMA for the characterization of a pelvic mass in both pre- and postmenopausal women. These findings suggest that HE4 and CA125 may not play an important role in the diagnosis of ovarian cancer if good quality ultrasonography is available.

Improving strategies for diagnosing ovarian cancer: a summary of the International Ovarian Tumor Analysis (IOTA) studies

In order to ensure that ovarian cancer patients access appropriate treatment to improve the outcome of this disease, accurate characterization before any surgery on ovarian pathology is essential. The International Ovarian Tumor Analysis (IOTA) collaboration has standardized the approach to the ultrasound description of adnexal pathology. A prospectively collected large database enabled previously developed prediction models like the risk of malignancy index (RMI) to be tested and novel prediction models to be developed and externally validated in order to determine the optimal approach to characterize adnexal pathology preoperatively. The main IOTA prediction models (logistic regression model 1 (LR1) and logistic regression model 2 (LR2)) have both shown excellent diagnostic performance (area under the curve (AUC) values of 0.96 and 0.95, respectively) and outperform previous diagnostic algorithms. Their test performance almost matches subjective assessment by experienced examiners, which is accepted to be the best way to classify adnexal masses before surgery. A two-step strategy using the IOTA simple rules supplemented with subjective assessment of ultrasound findings when the rules do not apply, also reached excellent diagnostic performance (sensitivity 90%, specificity 93%) and misclassified fewer malignancies than did the RMI. An evidence-based approach to the preoperative characterization of ovarian and other adnexal masses should include the use of LR1, LR2 or IOTA simple rules and subjective assessment by an experienced examiner.